We are concerned with the familiar and general case of approximating a probability distribution, such as occurs in Bayesian inference when both the prior over latent variables and the likelihood function connecting them to data are known, but computing the posterior exactly is intractable. There are two largely separate families of techniques for approximating such intractable inference problems: Markov Chain Monte Carlo (MCMC) sampling, and Variational Inference (VI) [1]}, [2]}.
